Sentence examples for ignoring features from inspiring English sources

Perhaps this should not be a surprise as shape alone (ignoring features) might not be expected to be a good discriminator of "actives" and "inactives".

Failure to account for all related soil features on a landscape by ignoring features below a fixed depth (e.g., 2 m) can result in (i) inconsistent genetic interpretations and (ii) an inaccurate and disjointed depiction of soils on the landscape.

Unexpectedly, while SC_All outperforms SC_GenProt, it is only by a small amount (~5% difference at the top 15 and 20% of predictions, Figure 5A), which indicates that we are losing little by ignoring features derived from experimental data.

And many of those models ignore features of the real world.Neoclassical models, for example, are built on the assumptions that individuals make their own decisions based on self-interest, that they seek to maximise their welfare and that the result is a stable system that tends to equilibrium.

Some cameras can recognise up to ten faces in a scene and set an average focus, or select just one face from a group and focus on that.To prevent the camera from locking on to faces in the background, the algorithms used in today's digicams tend to ignore features smaller than 10% of the viewfinder's height.

Because the relaxation ignores features of the original problem, it does not guarantee optimality.

In all of these accounts, there are common themes of omission: either abstract descriptions ignore features, or lack certain properties found in more concrete examples.

We revisit the association between milk production and reproductive performance using a novel interdisciplinary approach based on cutting-edge statistical methods that accommodate some of the unique and previously ignored features of this problem.

Simpler guidance would be to ignore features of iron deficiency entirely and recommend urgent referral of patients over 60 with unexplained anaemia on the haemoglobin value alone.

Such previously ignored features provide valuable insight for data quality control and validation of error-prone ESTs, as well as the ability to identify novel functional motifs embedded in large EST datasets.

Consequently, ConiferEST was designed to be a unique and complementary EST resource that can fill these gaps by focusing on the previously ignored features for the purposes of data quality control, validation and integration and for the possibility to explore new potential functional motifs embedded in large EST datasets.